Search NASA⌕ Search

SEARCH · Search NASA

Results for “hyperspectral data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15

SLICER Airborne Laser Altimeter Characterization of Canopy Structure and Sub-canopy Topography for the BOREAS Northern and Southern Study Regions: Instrument and Data Product Description

SLICER data were acquired in support of BOREAS at all of the TF sites in the SSA and NSA, and along transects between the study areas. Data were acquired on 5 days between 18-Jul and 30-Jul-1996. Each coverage of a tower site is typically 40 km in length, with a minimum of 3 and a maximum of 10 lines across each tower oriented in a variety of azimuths. The SLICER data were acquired simultaneously with ASAS hyperspectral, multiview angle images. The SLICER Level 3 products consist of binary files for each flight line with a data record for each laser shot composed of 13 parameters and a 600-byte waveform that is the raw record of the backscatter laser energy reflected from Earth's surface. The SLICER data are stored in a combination of ASCII and binary data files.

Hall, Forrest G.↗

Compact Hyperspectral Mapper for Environmental Remote Sensing Applications (CHyMERA) End-of-phase Data Review Package

CHYMERA is an Instrument Incubator concept to design, build, and test an instrument that will reduce size, mass, and cost and increase science potential and flexibility for future atmospheric remote sensing missions within the focus of NASA's Earth Science Enterprise (ESE). The primary effort of the development plan will be on high spatial resolution ozone, N02, S02, aerosol, and cloud measurements, but it is hoped that the techniques developed will prove useful for other measurements as well. The core design will involve a high performance, wide field-of-view (FOV) front end telescope which will illuminate a filter/focal plane array (FFPA) package. The use of a non-dispersive optical configuration will reduce size, mass and complexity. The wide FOV optics will permit short duration global coverage (1-2 days) without the need for a scanner.

Janz, Scott J.↗

ARM Data as A Resource for Validation of NASA PACE Cloud Retrievals

NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will launch in January 2024 and continue and improve upon satellite data records in its eponymous domains. PACE will carry a broad-swath hyperspectral imager, OCI, which will provide MODIS/VIIRS-type cloud data products (i.e. a cloud mask, top height, visible optical thickness, droplet effective radius, phase, and derived water path). It will also carry two multi-angle polarimeters (HARP2 and SPEXone) which will not only provide the above but also enable retrievals of additional cloud properties (e.g. droplet effective variance, ice crystal asymmetry parameter). Validating satellite-based cloud retrievals is challenging. We plan to use several ARM data streams to evaluate PACE cloud data products and are prototyping our analyses using retrievals from MODIS on the Aqua satellite and OLCI on the Sentinel-3A satellite. This poster shows how we plan to use ARM data to evaluate liquid water path (via MWRRET) and cloud top height retrievals (via KARZASRCL), with example results from these proxy sensors and ARM data from the SGP, ENA, and NSA sites. We seek comments from and collaborations with the ARM community to get the most out of our respective data streams.

ARM↗

High spectral resolution remote sensing of the earth

Early results of a major advance in remote sensing called imaging spectrometry (IS), the simultaneous acquisition of images in many narrow contiguous spectral bands throughout the visible and solar-reflected infrared portions of the spectrum, are discussed. The motivation for IS is reviewed, and the performance of IS is examined, describing prototype sensors. The analysis of IS data is addressed, including the problem of visual interaction with hyperspectral images and their statistical properties, and the effect of data dimensionality on multispectral scene classification. A test of the capability of imaging spectrometry to identify minerals is reported.

Vane, G.↗

An Investigation of the Characterization of Cloud Contamination in Hyperspectral Radiances

In regions lacking direct observations, the assimilation of radiances from infrared and microwave sounders is the primary method for characterizing the atmosphere in the analysis process. In recent years, technological advances have led to the launching of more advanced sounders, particularly in the thermal infrared spectrum. With the advent of these hyperspectral sounders, the amount of data available for the analysis process has and will continue to be dramatically increased. However, the utilization of infrared radiances in variational assimilation can be problematic in the presence of clouds; specifically the assessment of the presence of clouds in an instantaneous field of view (IFOV) and the contamination in the individual channels within the IFOV. Various techniques have been developed to determine if a channel is contaminated by clouds. The work presented in this paper and subsequent presentation will investigate traditional techniques and compare them to a new technique, the C02 sorting technique, which utilizes the high spectral resolution of the Atmospheric Infrared Sounder (AIRS) within the framework of the Gridpoint Statistical Interpolation (GSI) 3DVAR system. Ultimately, this work is done in preparation for the assessment of short-term forecast impacts with the regional assimilation of AIRS radiances within the analysis fields of the Weather Research and Forecast Nonhydrostatic Mesoscale Model (WRF-NMM) at the NASA Short-term Prediction Research and Transition (SPORT) Center.

McCarty, William↗

P161 Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

For over 6 years, AIRS radiances have been assimilated operationally into National (e.g. Environmental Modeling Center (EMC)) and International (e.g. European Centre for Medium-Range Weather Forecasts (ECMWF)), operational centers; assimilated in the North American Mesoscale (NAM) since 2008. Due partly to data latency and operational constraints, hyperspectral radiance assimilation has had less impact on the Gridpoint Statistical Interpolation (GSI) system used in the NAM and GFS. Objective of this project is to use AIRS retrieved profiles as a proxy for the AIRS radiances in situations where AIRS radiances are unable to be assimilated in the current operational system by evaluating location and magnitude of analysis increments.

Zavodsky, Bradley T.↗

Unmixing of Spectral Components Affecting AVIRIS Imagery of Tampa Bay

Models have been developed for use with hyperspectral remote-sensing reflectance data collected just above the air-sea interface for the West Florida Shelf 1,2,3,4,. They respond to variations in pigment, detrital, and gelbstoff absorption, chlorophyll a and gelbstoff flourescence, water Raman Scattering, Backscattering by water and particulate, and bottom depth and albedo.

Imagery↗

Coordinated Microstructural and Isotopic Characterization of Diamond Inclusions within the Kenna Ureilite

Introduction: The origin of diamonds in ureilitic meteorites, a major family of ultramafic achondrites, has long been debated, with various formation mechanisms inferred, including: 1) shock conversion of preexisting graphite due to a hypervelocity impact on the ureilite parent body [e.g., 1]; 2) vapor deposition during condensation of the solar nebula [e.g., 2]; or 3) static transformation in the mantle of a large planetesimal (>1000 km in diameter) [e.g., 3]. The Kenna ureilite is a 10.9 kg stone that was found in Kansas in 1972 and is dominantly composed of olivine and pigeonite (68% and 22% modal abundance, respectively), together with minor metal and carbon-rich regions. Within the carbon-rich domains researchers have identified microdiamonds up to ~10 µm in size [4]. Methods: Due to the proximity of the extremely hard microdiamonds with soft graphite and moderately hard silicates, achieving an appropriately flat surface for electron backscatter diffraction (EBSD) analysis has proven challenging. We have, therefore, employed a Hitachi ArBlade 5000 broad beam Ar ion milling system to prepare a sample with an adequately uniform surface flatness across the various minerals. The ion milling dramatically im-proved indexing of the EBSD patterns and facilitated high-resolution microstructural analysis of the carbon-rich domains. After microstructural EBSD analyses using an Oxford Symmetry detector we collected coordinated hy-perspectral cathodoluminescence (CL) analyses using a Gatan Monarc system mounted on a JEOL 7900F field emission (FE) scanning electron microscope at NASA Johnson Space Center. A focused ion beam (FIB) foil was extracted from a region of interest and analyzed using a JEOL 2500 FE scanning transmission electron microscope (STEM). In situ carbon and nitrogen isotopic analyses were collected from diamond-bearing domains using a Cameca NanoSIMS 50L at NASA JSC. Results: The carbon rich regions occur as tabular domains between polygonal olivine and pigeonite, and are composed of aggregates of micrometer-scale diamond with intermediate graphite, oxides and metal grains. Within the microdiamonds, {111} twins are abundant, many of which form ragged lamellae consistent with mechanical twinning. The microdiamond assemblages appear to pseudomorph tabular graphite habits, which together with the abundant deformation twins, support a shock transformation mechanism for microdiamond formation within the Kenna meteorite. Further supporting this observation, the diamond laths show close alignment of <111> and <110> between domains, consistent with a solid-state transformation from graphite along <0001> and <1-210>, respec-tively [5]. A FIB foil was extracted from a region with mechanical twins, and high resolution STEM analyses reveal cross-cutting veins of graphite and oriented kamacite crystals together with additional sets of {111} nano-twins formed in at least three orientations. Isotopic analyses of C and N reveal sector zoning patterns [cf. 3] that are unre-lated to the diamond substructure identified by EBSD yet consistent with hyperspectral CL images. Conclusions: These data provide new constraints on microdiamond formation within ureilites. The microdia-monds exhibit orientation variants defined by aligned <111> and <110> axes consistent with inherited crystallo-graphic orientations from transformation of a single hexagonal graphite parent crystal [5]. Crosscutting the micro-diamonds are veins of graphite and kamacite, together with additional {111} nanotwins. Taken together these data suggest that microdiamonds from ureilites formed from a solid state, diffusionless transformation from graphite, likely associated with a major shock event, which was subject to subsequent reversion either during decompression or a later deformation event. The N and C isotopic sector zoning is unrelated to diamond microstructures and likely reflects inheritance from the parent graphite crystal.

Ureilite↗

An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

classification↗

Lunar and Planetary Science XXXV: Mars: Remote Sensing and Terrestrial Analogs

The session "Mars: Remote Sensing and Terrestrial Analogs" included the following:Physical Meaning of the Hapke Parameter for Macroscopic Roughness: Experimental Determination for Planetary Regolith Surface Analogs and Numerical Approach; Near-Infrared Spectra of Martian Pyroxene Separates: First Results from Mars Spectroscopy Consortium; Anomalous Spectra of High-Ca Pyroxenes: Correlation Between Ir and M ssbauer Patterns; THEMIS-IR Emissivity Spectrum of a Large Dark Streak near Olympus Mons; Geomorphologic/Thermophysical Mapping of the Athabasca Region, Mars, Using THEMIS Infrared Imaging; Mars Thermal Inertia from THEMIS Data; Multispectral Analysis Methods for Mapping Aqueous Mineral Depostis in Proposed Paleolake Basins on Mars Using THEMIS Data; Joint Analysis of Mars Odyssey THEMIS Visible and Infrared Images: A Magic Airbrush for Qualitative and Quantitative Morphology; Analysis of Mars Thermal Emission Spectrometer Data Using Large Mineral Reference Libraries ; Negative Abundance : A Problem in Compositional Modeling of Hyperspectral Images; Mars-LAB: First Remote Sensing Data of Mineralogy Exposed at Small Mars-Analog Craters, Nevada Test Site; A Tool for the 2003 Rover Mini-TES: Downwelling Radiance Compensation Using Integrated Line-Sight Sky Measurements; Learning About Mars Geology Using Thermal Infrared Spectral Imaging: Orbiter and Rover Perspectives; Classifying Terrestrial Volcanic Alteration Processes and Defining Alteration Processes they Represent on Mars; Cemented Volcanic Soils, Martian Spectra and Implications for the Martian Climate; Palagonitic Mars: A Basalt Centric View of Surface Composition and Aqueous Alteration; Combining a Non Linear Unmixing Model and the Tetracorder Algorithm: Application to the ISM Dataset; Spectral Reflectance Properties of Some Basaltic Weathering Products; Morphometric LIDAR Analysis of Amboy Crater, California: Application to MOLA Analysis of Analog Features on Mars; Airborne Radar Study of Soil Moisture at a Mars Analog Site: Tohachi Wash/Little Colorado River; and Antarctic Dry Valleys: Modification of Rocks and Soils and Implications for Mars The Arkaroola Mars Analogue Region, South Australia.

Source record↗

Low-Complexity Adaptive Lossless Compression of Hyperspectral Imagery

A low-complexity, adaptive predictive technique for lossless compression of hyperspectral imagery is described. This technique is designed to be suitable for implementation in hardware such as a field programmable gate array (FPGA); such an implementation could be used for high-speed compression of hyperspectral imagery onboard a spacecraft. The predictive step of the technique makes use of the sign algorithm, which is a relative of the least mean square (LMS) algorithm from the field of low-complexity adaptive filtering. The compressed data stream consists of prediction residuals encoded using a method similar to that of the JPEG-LS lossless image compression standard. Compression results are presented for several datasets including some raw Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) datasets and raw Atmospheric Infrared Sounder (AIRS) datasets. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.

lossless data compression↗

Recent Experiments Conducted with the Wide-Field Imaging Interferometry Testbed (WIIT)

The Wide-field Imaging Interferometry Testbed (WIIT) was developed at NASA's Goddard Space Flight Center to demonstrate and explore the practical limitations inherent in wide field-of-view double Fourier (spatio-spectral) interferometry. The testbed delivers high-quality interferometric data and is capable of observing spatially and spectrally complex hyperspectral test scenes. Although WIIT operates at visible wavelengths, by design the data are representative of those from a space-based far-infrared observatory. We used WIIT to observe a calibrated, independently characterized test scene of modest spatial and spectral complexity, and an astronomically realistic test scene of much greater spatial and spectral complexity. This paper describes the experimental setup, summarizes the performance of the testbed, and presents representative data.

Leisawitz, David T.↗

Great Lakes Hyperspectral Water Quality Instrument Suite for Airborne Monitoring of Algal Blooms

NASA Glenn Research Center and NOAA Great Lakes Environmental Research Lab are collaborating to utilize an airborne hyperspectral imaging sensor suite to monitor Harmful Algal Blooms (HABs) in the western basin of Lake Erie. The HABs are very dynamic events as they form, spread and then disappear within a 4 to 8 week time period in late summer. They are a concern for human health, fish and wildlife because they can contain blue green toxic algae. Because of this toxicity there is a need for the blooms to be continually monitored. This situation is well suited for aircraft based monitoring because the blooms are a very dynamic event and they can spread over a large area. High resolution satellite data is not suitable by itself because it will not give the temporal resolution due to the infrequent overpasses of the quickly changing blooms. A custom designed hyperspectral imager and a point spectrometer mounted on aT 34 aircraft have been used to obtain data on an algal bloom that formed in the western basin of Lake Erie during September 2006. The sensor suite and operations will be described and preliminary hyperspectral data of this event will be presented

Lekki, John↗

Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests

Light detection and ranging (LiDAR) data can provide direct measurements of vegetation structures but are limited by the sparse spatial coverage. Polarimetric synthetic aperture radar (SAR) can perform large-scale high-resolution mapping without weather constraints but the information about vegetation and ground subsurface are mixed in the backscatter data. In this paper, we adopted the Random Forests algorithm to train an upscaling function using tree canopy cover (TCC) and canopy height model (CHM) derived from Goddard’s LiDAR, Hyperspectral and Thermal Imager (G-LiHT) data. The regression model is then applied to the L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign to map the TCC and CHM over the Delta Junction area in interior Alaska.

Moghaddam, Mahta↗

Low-Cost Evaluation of EO-1 Hyperion and ALI for Detection and Biophysical Characterization of Forest Logging in Amazonia (NCC5-481)

Major uncertainties exist regarding the rate and intensity of logging in tropical forests worldwide: these uncertainties severely limit economic, ecological, and biogeochemical analyses of these regions. Recent sawmill surveys in the Amazon region of Brazil show that the area logged is nearly equal to total area deforested annually, but conversion of survey data to forest area, forest structural damage, and biomass estimates requires multiple assumptions about logging practices. Remote sensing could provide an independent means to monitor logging activity and to estimate the biophysical consequences of this land use. Previous studies have demonstrated that the detection of logging in Amazon forests is difficult and no studies have developed either the quantitative physical basis or remote sensing approaches needed to estimate the effects of various logging regimes on forest structure. A major reason for these limitations has been a lack of sufficient, well-calibrated optical satellite data, which in turn, has impeded the development and use of physically-based, quantitative approaches for detection and structural characterization of forest logging regimes. We propose to use data from the EO-1 Hyperion imaging spectrometer to greatly increase our ability to estimate the presence and structural attributes of selective logging in the Amazon Basin. Our approach is based on four "biogeophysical indicators" not yet derived simultaneously from any satellite sensor: 1) green canopy leaf area index; 2) degree of shadowing; 3) presence of exposed soil and; 4) non-photosynthetic vegetation material. Airborne, field and modeling studies have shown that the optical reflectance continuum (400-2500 nm) contains sufficient information to derive estimates of each of these indicators. Our ongoing studies in the eastern Amazon basin also suggest that these four indicators are sensitive to logging intensity. Satellite-based estimates of these indicators should provide a means to quantify both the presence and degree of structural disturbance caused by various logging regimes. Our quantitative assessment of Hyperion hyperspectral and ALI multi-spectral data for the detection and structural characterization of selective logging in Amazonia will benefit from data collected through an ongoing project run by the Tropical Forest Foundation, within which we have developed a study of the canopy and landscape biophysics of conventional and reduced-impact logging. We will add to our base of forest structural information in concert with an EO-1 overpass. Using a photon transport model inversion technique that accounts for non-linear mixing of the four biogeophysical indicators, we will estimate these parameters across a gradient of selective logging intensity provided by conventional and reduced impact logging sites. We will also compare our physical ly-based approach to both conventional (e.g., NDVI) and novel (e.g., SWIR-channel) vegetation indices as well as to linear mixture modeling methods. We will cross-compare these approaches using Hyperion and ALI imagers to determine the strengths and limitations of these two sensors for applications of forest biophysics. This effort will yield the first physical ly-based, quantitative analysis of the detection and intensity of selective logging in Amazonia, comparing hyperspectral and improved multi-spectral approaches as well as inverse modeling, linear mixture modeling, and vegetation index techniques.

Asner, Gregory P.↗

Combining S-space and image spatial information for characterization of land-cover elements using AVIRIS data

Imaging spectroscopy data provides information that is both spatially and spectrally rich. However,hyperspectral classification and identification procedures are generally based solely on the spectral data. The S-space concept treats the spectrum as a 'spatial' dimension thereby facilitating the exploration of a relationship between the spectral and spatial image domains. This paper presents the ideas that will be researched as well as some preliminary results.

AVIRIS image spectroscopy↗

Soil Salinity Level Assessment and Prediction Integrating UAV-borne Hyperspectral Imaging and Machine Learning Algorithms to Combat Desertification

In response to the ongoing global food crisis, the United Nations has identified “Zero Hunger” as one of its Sustainable Development Goals. A central contributor to the crisis is the process in which agricultural lands go through desertification. Research has shown a direct correlation between soil salinity and desertification - increased salinity levels indicate a higher risk for desertification. Furthermore, researchers have explored various techniques to map soil salinity, but these methods are oftentimes inefficient and don’t address future salinity predictions. To improve desertification monitoring, soil salinity can be observed via hyperspectral imaging on unmanned aerial vehicles (UAVs) to predict the risk of agricultural desertification using artificial intelligence (AI) and machine learning (ML) techniques. A significant gap exists in past research that applies ML and imaging techniques to soil salinity: convolutional neural networks (CNNs) and regression models are rarely leveraged together, despite the efficiency and accuracy of these models. To compensate for this gap, the proposed system leverages the use of these AI and ML models to improve soil assessment and prediction techniques. This approach involves three steps - data collection, image analysis, and future prediction. Using hyperspectral cameras on UAVs to collect the data from the region, a trained CNN model will output estimated soil salinity levels at a specific time. The estimations will then be analyzed by a regression model to assess the accuracy of future soil salinity predictions. The proposed system will identify regions at risk of desertification to help farmers mitigate agricultural loss, in turn helping alleviate the food crisis.

UAV systems↗